Mitigating the risk of opioid abuse through a balanced undergraduate pain medicine curriculum
Bibliographic record
Abstract
Patricia K Morley-Forster,1,2 Joseph V Pergolizzi,3–5 Robert Taylor Jr,5 Robert A Axford-Gatley,6 Edward M Sellers71Department of Anesthesia and Perioperative Medicine, University of Western Ontario, London, ON, Canada; 2Outpatient Pain Clinic, St Joseph’s Hospital, London, ON, Canada; 3Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; 4Department of Pharmacology, Temple University School of Medicine, Philadelphia, PA, USA; 5NEMA Research Inc, Naples, FL, USA; 6Clinical Content and Editorial Services, Complete Healthcare Communications, Inc, Chadds Ford, PA, USA; 7DL Global Partners Inc, Toronto, ON, CanadaAbstract: Chronic pain is highly prevalent in the United States and Canada, occurring in an estimated 30% of the adult population. Despite its high prevalence, US and Canadian medical schools provide very little training in pain management, including training in the safe and effective use of potent analgesics, most notably opioids. In 2005, the International Association for the Study of Pain published recommendations for a core undergraduate pain management curriculum, and several universities have implemented pilot programs based on this curriculum. However, when outcomes have been formally assessed, these initiatives have resulted in only modest improvements in physician knowledge about chronic pain and its treatment. This article discusses strategies to improve undergraduate pain management curricula and proposes areas in which those efforts can be augmented. Emphasis is placed on opioids, which have great potency as analgesics but also substantial risks in terms of adverse events and the risk of abuse and addiction. The authors conclude that the most important element of an undergraduate pain curriculum is clinical experience under mentors who are capable of reinforcing didactic learning by modeling best practices.Keywords: chronic pain, curricular content, medical education, opioids, pain education, pain knowledge, physician training, teaching
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".